PROJECT SUMMARY/ABSTRACT In this Phase I SBIR application, we propose to develop and optimize functional Object Oriented Data Analysis (fOODA) methods and create a cloud-based SaaS platform to analyze untargeted metabolomics data. Untargeted metabolomics quantifies the amount of known and unknown metabolites in samples with the purpose of finding known and unknown metabolites that correlate with subgroups (e.g., diseased or healthy tissue). The goal of this research is to identify biomarkers for diagnosis and treatment targets. However, the biostatistical tools being used have not been optimized for this data, and so analyses lag behind the technical development. Functional OODA is an emerging area of statistics for analyzing functional data, such as untargeted metabolomic retention time x m/z intensity data metabolic data. The goal of this project is to optimize fOODA for this data to provide more powerful statistical methods for investigators. This should help them identify metabolite biomarkers more efficiently and avoid incorrectly calling unimportant metabolites as clinically relevant. Incorrect or inferior analyses results in loss of money and time running downstream experiments to validate these false positive calls. The innovation of this proposal is three-fold: (1) development of statistical models and methods specifically designed and optimized for raw (not pre-processed) untargeted metabolomics intensity data, (2) development of methods that allow inclusion of metadata and other omics data to measure how these factors impact metabolomic profiles, and (3) implementation of the methods in a cloud-based SaaS solution. The impact on metabolomics will be new tools for rigorous experimental design, hypothesis testing, and biostatistical analysis for discovery of disease biomarkers, and to move preclinical discoveries from the lab to the clinic faster. BioRankings has filed patent protection for this technology.